Digital Systems BiologySystems biology refers to multidisciplinary approaches designed to uncover emergent properties of complex biological systems. The aim of systems biology is to obtain a system-level understanding of such systems, by examining the structure and dynamics of cellular and organismal functions, rather than the characteristics of isolated parts of a cell or an organism (Kitano, 2002).
At the core of systems biology lies the construction of models describing biological systems. Two kinds of models for biological systems are distinguished: operational versus denotational. On the one hand, operational models (such as Petri nets) are executable and mimic biological processes. On the other hand, denotational models (such as differential equations) express mathematical relationships between quantities and how they change over time. Denotational models are in general quantitative, and in systems biology tend to require a lot of computation power to simulate, let alone to solve mathematically. Also it is often practically impossible to obtain the precise quantitative information needed for such models. Operational models are in general qualitative, and are thus at a higher abstraction level and easier to analyse.
Operational models of biological systems are the standard models of computational systems as studied in computer science/engineering. As a matter of fact, to recover temporal, spatial, and causal information on biological systems, well-established computing techniques, collectively referred to as formal methods, that deal with program analysis, composition, verification, etc. can be employed.
The use of operational models together with the methodology of computer systems engineering supported by formal methods is an emerging field that dramatically enhances current analysis capabilities to increase our understanding of complex living systems. The technological impact of merging computer science and systems biology will be the design and implementation of digital biology laboratories capable of performing many more experiments than what is currently feasible in real labs and at lower cost (in terms both of human and financial resources) and in less time. These labs will allow biologists to design, execute, and analyze experiments to generate new hypotheses and develop novel high-throughput tools, resulting in advances in experimental design, documentation, and interpretation as well as a deeper integration between ``wet'' (lab-based) and ``dry'' research. Moreover, the digital biology laboratories will be a main vehicle for moving from single-gene diseases to multifactorial diseases, which account for more than 90% of the illnesses affecting our society.
Main research prioritiesOur long-term research goal is to develop and apply computational science and technology to enhance our understanding of the molecular mechanisms underlying the behavior of living systems and develop scalable methods and tools for modeling and computerized analysis of large and complex living systems.
- Scalable methods and tools for modeling complex systems. To deal
with the complexity of biological systems, which is typically induced by the interaction
of several interwoven modules with complex dynamic behavior, acting on different
time scales, we need to apply mathematical modeling and computer-supported
analysis. We must develop new, scalable methods for modeling and analysis of
The main goal is to adapt formal methods, like model-checking and state-space analysis algorithms, to fit the needs of effective qualitative analysis of regulatory networks dymamics. In particular, we aim at developping parallel and distributed algorithms for automatic analysis of specific properties of biological models.
- Parameter estimation in biological models. Systems Biology models
often have numerous parameters, such as kinetic constants, decay rates and
drift/diffusion terms, which are unknown or only weakly constrained by existing
experimental knowledge. A crucial problem for Systems Biology is that these
parameters are often very difficult to measure directly. Furthermore, they may
vary greatly according to their in vivo context. As a result, methods for the
estimation of these parameters are of great interest.
The goal of our research is to develop effective, fast, and scalable methods, techniques and tools for automated parameter estimation for computational analysis of biological systems.
- Digital systems biology laboratory. Our goal is to develop a tool integration framework which is targeted towards modeling, simulation, visualisation, and analysis of complex biological systems. The framework will allow the instantiation of different tool chains supporting various process flows for which different tools and combination of tools are required.